Bibliographic record
Abstract
At this point I would like to express my gratitude to everyone that helped me in the process of writing this thesis. First, and definitely most important, I want to thank my supervisor, Univ.-Prof. Dr. Alexander Ostermann, for the supervision and tremendous support throughout the development of my thesis and beyond. I also want to thank Dipl-Ing. Stefan Rainer for introducing me to the topic of my thesis, the fruitful discussions, as well as the enormous patience and time he devoted to my questions and problems. As parts of this thesis were developed at the McMaster University in Canada I want to thank my supervisor Prof. Nicholas Kevlahan, for supporting me not only during the research, but also during my whole exchange at McMaster. I also want to take this opportunity to thank the Joint Study Program of the University of Innsbruck that made the one year exchange possible. My gratitude also goes to my colleagues at both Universities like Katharina, Christian, Georg, Tobias and Julian, for supporting me during my studies with an open ear for questions and welcome distractions, if needed. For the technical support with the computation systems used in the example part of this thesis, I want to thank Martin Pöll and the whole ZID team of the University of Innsbruck. Apart of this I want to thank Linda Quehenberger and Andreas Holzmann for linguistic assistance. My gratitude also goes to my family and friends for the support and giving me the opportunity to study. I want to thank the Tiroler Wissenschaftsfond for the financial support
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.326 | 0.257 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".